Mission: Iconic Reefs and National Coral Reef Monitoring Program Benthic Analysis

Summary Report for 2022 and 2024

NOAA Logo
Mission Iconic Reef Logo
Authors:
Nicole Krampitz1, Julia Mateski1, Dione Swanson2,3
1 CSS, Inc. under contract to NOAA National Ocean Service, National Centers for Coastal Ocean Science
2 NOAA National Marine Fisheries Service, Southeast Fisheries Science Center
3 University of Miami, Cooperative Institute for Marine and Atmospheric Studies
Point of Contact: Shay Viehman (NCRMP Atlantic Benthic Team Lead) shay.viehman@noaa.gov
Data Sources: National Coral Reef Monitoring Program Florida Benthic Data

1 Introduction

The Mission: Iconic Reefs (M:IR) project is a decades-long ambitious effort to restore seven key coral reef areas in the Florida Keys National Marine Sanctuary (FKNMS) via coral propagation and transplantation. Restoration is being carried out in planned phases along with biennial monitoring to provide a quantitative measure of restoration effects over time. The primary goal of this monitoring is to compare fish populations, coral populations, and benthic community metrics within M:IR restoration areas to those reefs in the broader Florida Keys in partnership with the National Coral Reef Monitoring Program (NCRMP). Currently, two rounds of surveying have been completed (in 2022 and 2024) to establish baseline conditions. This report focuses on coral populations and benthic community metrics; a separate report can be found for fish population assessments.

Figure 1: Map of the Florida Reef Tract with Specified M:IR Restoration Sites

Corals and benthic communities were monitored using two different surveys: a Benthic Community Assessment survey and a Coral Demographics survey (NOAA CRCP, 2022b, 2022c). The Benthic Community Assessment survey includes: 1) benthic cover (%) estimates along a 15-m transect with a line point intercept method, 2) presence/absence of Endangered Species Act (ESA)-listed coral species (NOAA National Marine Fisheries Service, 2014), 3) abundance of key macroinvertebrates, and 4) reef rugosity measurements within a 15 m x 2 m belt-transect area (NOAA CRCP, 2022b). At the same site, coral demographics were surveyed within a 10 m x 1 m belt-transect area (NOAA CRCP, 2022c). NCRMP coral demographic survey data was combined with complementarily allocated survey data from Florida Reef Resiliency Program’s Disturbance Response Monitoring (DRM). In all Coral Demographics surveys, all live coral colonies ≧ 4 cm were counted, identified to species, measured to the nearest centimeter (length, width, height), and estimates were made of the proportion per colony of any present mortality (recent or old), disease (present, slow, fast), and/or bleaching (total, partial, paling). Only live coral colonies were included in the survey; dead colonies with 100% mortality were not surveyed (e.g., colonies killed by coral disease). Juvenile corals (< 4 cm) were reported for species richness only and were not included in counts, size measurements, or estimates of condition.

A majority of the reef habitats within the seven M:IR areas ranged from 0-12 meters depth. For comparison, survey data from the NCRMP were restricted to the same depth range. The tables and figures below include: Sampling effort by strata and survey year and species-specific estimates of colony density, mean size, partial mortality, size frequency, and prevalence of bleaching and disease. Statistical comparisons were conducted between M:IR and NCRMP estimates for each survey year and between years for M:IR and NCRMP.

2 Benthic Cover Sites

Table 1. Number of benthic cover sites sampled inside and outside M:IR areas in each stratum.
Study Area Strata Name Strat Description 2022 Sample Number 2024 Sample Number
Inside (M:IR) CFK01 Inshore reef, all relief types and depths 8 10
Inside (M:IR) CFK02 Mid-channel patch reef, all relief types and depths 12 12
Inside (M:IR) CFK03 Offshore patch reef, all relief types and depths 7 8
Inside (M:IR) CFK04 Forereef, low relief, shallow (0-6 m) NA 3
Inside (M:IR) CFK05 Forereef, high relief, shallow (0-6 m) 24 33
Inside (M:IR) CFK06 Forereef, low relief, mid-shallow (6-12 m) 4 9
Inside (M:IR) CFK07 Forereef, high relief, mid-shallow (6-12 m) 32 28
Outside (NCRMP) CFK01 Inshore reef, all relief types and depths 10 9
Outside (NCRMP) CFK02 Mid-channel patch reef, all relief types and depths 21 13
Outside (NCRMP) CFK03 Offshore patch reef, all relief types and depths 9 8
Outside (NCRMP) CFK04 Forereef, low relief, shallow (0-6 m) NA 6
Outside (NCRMP) CFK05 Forereef, high relief, shallow (0-6 m) 7 9
Outside (NCRMP) CFK06 Forereef, low relief, mid-shallow (6-12 m) 9 7
Outside (NCRMP) CFK07 Forereef, high relief, mid-shallow (6-12 m) 10 28

3 Coral Demographic Sites

Table 2. The number of coral demographic sites sampled inside and outside M:IR areas in each stratum (includes DRM data).
Study Area Strata Name Strat Description 2022 Sample Number 2024 Sample Number
Inside (M:IR) CFK01 Inshore reef, all relief types and depths 12 10
Inside (M:IR) CFK02 Mid-channel patch reef, all relief types and depths 16 12
Inside (M:IR) CFK03 Offshore patch reef, all relief types and depths 7 11
Inside (M:IR) CFK04 Forereef, low relief, shallow (0-6 m) NA 4
Inside (M:IR) CFK05 Forereef, high relief, shallow (0-6 m) 25 38
Inside (M:IR) CFK06 Forereef, low relief, mid-shallow (6-12 m) 4 12
Inside (M:IR) CFK07 Forereef, high relief, mid-shallow (6-12 m) 34 34
Outside (NCRMP + DRM) CFK01 Inshore reef, all relief types and depths 27 24
Outside (NCRMP + DRM) CFK02 Mid-channel patch reef, all relief types and depths 79 109
Outside (NCRMP + DRM) CFK03 Offshore patch reef, all relief types and depths 37 42
Outside (NCRMP + DRM) CFK04 Forereef, low relief, shallow (0-6 m) 4 31
Outside (NCRMP + DRM) CFK05 Forereef, high relief, shallow (0-6 m) 21 45
Outside (NCRMP + DRM) CFK06 Forereef, low relief, mid-shallow (6-12 m) 19 33
Outside (NCRMP + DRM) CFK07 Forereef, high relief, mid-shallow (6-12 m) 60 61

4 Most Common and Focal M:IR Species

M:IR selected 10 focal coral species for comparison to NCRMP: Acropora palmata, Acropora cervicornis, Colpophyllia natans, Dendrogyra cylindrus, Dichocoenia stokesii, Diploria labyrinthiformis, Meandrina meandrites, Montastraea cavernosa, Pseudodiploria clivosa, and Pseudodiploria strigosa. The following table summarizes the number of observations across all stratum and survey years of each focal species and identifies the most abundant species encountered.

Table 3. Most Observed Species and Focal M:IR Species
Species Species Code Number of Colonies Observed
Siderastrea siderea SID SIDE 9486
Porites astreoides POR ASTR 4789
Stephanocoenia intersepta STE INTE 2879
Porites porites POR PORI 1334
Agaricia agaricites AGA AGAR 1121
*Montastraea cavernosa MON CAVE 1082
Siderastrea radians SID RADI 510
Orbicella faveolata ORB FAVE 503
Orbicella annularis ORB ANNU 367
*Dichocoenia stokesii DIC STOK 243
Porites furcata POR FURC 237
Porites divaricata POR DIVA 218
Solenastrea bournoni SOL BOUR 192
*Colpophyllia natans COL NATA 179
Eusmilia fastigiata EUS FAST 120
*Diploria labyrinthiformis DIP LABY 115
*Pseudodiploria strigosa PSE STRI 96
*Acropora cervicornis ACR CERV 87
*Pseudodiploria clivosa PSE CLIV 54
*Meandrina meandrites MEA MEAN 16
*Acropora palmata ACR PALM 14
* indicates M:IR focal species

5 Benthic Cover Composition

Temporal trends in benthic cover of functional groups such as coral and macroalgae cover provide insight …..

Figure 2. Hard coral and macroalgae cover estimates inside and outside M:IR areas for 2022 and 2024. Significant difference in cover estimates in each of these areas was indicated by (*).

6 Species Specific Plots

Insert more text here. Sentence about surveys designed to produce population estimates of coral species. Metrics for status, trends and restoration effects(?) - density, partial mortality, mean colony size, colony size frequency, and colony size partial mortality. Only focal M:IR species are reported below

The following figures for these metrics are grouped by each focal species. Significant statistical differences are indicated for differences between survey years and between areas (MIR vs. NCRMP) for density, old mortality, and mean colony size.

Figures 3a-3i. Coral density, old mortality, colony size, and size frequency distribution for focal M:IR species across sample years (2022 and 2024) and survey types (MIR and NCRMP + DRM). Significant difference in cover estimates in each of these areas was indicated by (*).

Figures 3a-3i. Coral density, old mortality, colony size, and size frequency distribution for focal M:IR species across sample years (2022 and 2024) and survey types (MIR and NCRMP + DRM). Significant difference in cover estimates in each of these areas was indicated by (*).

Figures 3a-3i. Coral density, old mortality, colony size, and size frequency distribution for focal M:IR species across sample years (2022 and 2024) and survey types (MIR and NCRMP + DRM). Significant difference in cover estimates in each of these areas was indicated by (*).

Figures 3a-3i. Coral density, old mortality, colony size, and size frequency distribution for focal M:IR species across sample years (2022 and 2024) and survey types (MIR and NCRMP + DRM). Significant difference in cover estimates in each of these areas was indicated by (*).

Figures 3a-3i. Coral density, old mortality, colony size, and size frequency distribution for focal M:IR species across sample years (2022 and 2024) and survey types (MIR and NCRMP + DRM). Significant difference in cover estimates in each of these areas was indicated by (*).

Figures 3a-3i. Coral density, old mortality, colony size, and size frequency distribution for focal M:IR species across sample years (2022 and 2024) and survey types (MIR and NCRMP + DRM). Significant difference in cover estimates in each of these areas was indicated by (*).

Figures 3a-3i. Coral density, old mortality, colony size, and size frequency distribution for focal M:IR species across sample years (2022 and 2024) and survey types (MIR and NCRMP + DRM). Significant difference in cover estimates in each of these areas was indicated by (*).

Figures 3a-3i. Coral density, old mortality, colony size, and size frequency distribution for focal M:IR species across sample years (2022 and 2024) and survey types (MIR and NCRMP + DRM). Significant difference in cover estimates in each of these areas was indicated by (*).

Figures 3a-3i. Coral density, old mortality, colony size, and size frequency distribution for focal M:IR species across sample years (2022 and 2024) and survey types (MIR and NCRMP + DRM). Significant difference in cover estimates in each of these areas was indicated by (*).

7 Bleaching Estimates

7.1 2022

Code
# Set path to data files
data_2022_path <- "data/FK2022_NCRMP_DRM_MIR_corsz_ARallv2.csv"



MIR_species <- c("ACR PALM", "ACR CERV", "COL NATA", "DEN CYLI", "DIC STOK", "DIP LABY", "EUS FAST", "MEA MEAN", "PSE CLIV", "PSE STRI", "MON CAVE")

#Script works for the actual value but CI are not exact
# tmp <- disease_bleach_prevalence_ratio_est(data_2022_path, 2022) %>%
#   filter(PV_TYPE == "Bleach_All") %>%
#   left_join(., names)


names_clean <- names %>%
  select(SPECIES_CD, SPECIES_NAME) %>%
  distinct(SPECIES_CD, .keep_all = TRUE)

tmp_ble_2022 <- read_csv("../Project/data/fk2022_NCRMP_MIR_sppPVbar_Ball_dom.csv") %>%
  left_join(., names_clean, by = "SPECIES_CD")

top_species <- tmp_ble_2022 %>%
  group_by(SPECIES_CD) %>%
  summarise(mean_pv = mean(PVbar, na.rm = TRUE)) %>%
  arrange(desc(mean_pv)) %>%
  slice_head(n = 10) %>%  
  pull(SPECIES_CD)

tmp_filtered <- tmp_ble_2022 %>%
  filter(SPECIES_CD %in% union(top_species, MIR_species)) 

##Add a * for those MIR species
tmp_filtered <- tmp_filtered %>% mutate(
  SPECIES_NAME = case_when(
    SPECIES_CD %in% MIR_species ~ paste0("*", SPECIES_NAME),
    TRUE ~ as.character(SPECIES_NAME)))

#Factor re-order for aesthetics  
tmp_filtered <- tmp_filtered %>%
  mutate(SPECIES_NAME = 
           fct_reorder(SPECIES_NAME, if_else(is.na(PVbar), 0, PVbar), .desc = FALSE)) %>%
    mutate(analysis_group = recode(analysis_group,
                                 "MIR_GRP" = "MIR",
                                 "NCRMP_GRP" = "NCRMP + DRM"))
  

plt_bleach <- tmp_filtered %>%
  filter(SPECIES_NAME != "NA") %>%
  ggplot(aes(x = PVbar, y = SPECIES_NAME, fill = analysis_group )) +
  geom_col(position = position_dodge(width = 0.8), width = 0.8) +
  scale_fill_manual(values = c("#0072B2", "#D55E00"), labels = c("MIR", "NCRMP + DRM")) +
  geom_errorbar(aes(xmin = PV_LCI, xmax = PV_UCI),
                position = position_dodge(width = 0.8), width = 0.1) +
  facet_wrap(~analysis_group) +
    labs(x = "Proportion of Colonies Bleached", y = "Coral Species", fill = "") +
    scale_x_continuous(expand = expansion(mult = c(0, 0.1))) +
        theme_Publication(base_size = 10) +
    coord_cartesian(clip = "off") +
        scale_y_discrete(labels = function(x) parse(text = paste0("italic('", x, "')"))) +
    theme(
          strip.text = element_text(size = 14, face = "bold"),
          axis.text.y = element_text(size = 11),
          axis.title = element_text(size = 13),
          legend.position = "bottom",
          legend.text = element_text(size = 10),
          panel.spacing = unit(1, "lines")
        ) #+
 # ggtitle("2022 Estimates")


print(plt_bleach)

Figure 4. Estimated Observed Bleaching Frequency in 2022 using Ratio Estimaters.

7.2 2024

Code
# Set path to data files
data_2022_path <- "data/FK2022_NCRMP_DRM_MIR_corsz_ARallv2.csv"


MIR_species <- c("ACR PALM", "ACR CERV", "COL NATA", "DEN CYLI", "DIC STOK", "DIP LABY", "EUS FAST", "MEA MEAN", "PSE CLIV", "PSE STRI", "MON CAVE")

#same as section above
# tmp <- disease_bleach_prevalence_ratio_est(data_2022_path, 2022) %>%
#   filter(PV_TYPE == "Bleach_All") %>%
#   left_join(., names)

names_clean <- names %>%
  select(SPECIES_CD, SPECIES_NAME) %>%
  distinct(SPECIES_CD, .keep_all = TRUE)

tmp <- read_csv("../Project/data/fk2024_NCRMP_MIR_sppPVbar_Ball_dom.csv") %>%
    left_join(names_clean, by = "SPECIES_CD")

top_species <- tmp %>%
  group_by(SPECIES_CD) %>%
  summarise(mean_pv = mean(PVbar, na.rm = TRUE)) %>%
  arrange(desc(mean_pv)) %>%
  slice_head(n = 10) %>%  
  pull(SPECIES_CD)

tmp_filtered <- tmp %>%
  filter(SPECIES_CD %in% union(top_species, MIR_species)) 
  
##Add a * for those MIR species
tmp_filtered <- tmp_filtered %>% mutate(
  SPECIES_NAME = case_when(
    SPECIES_CD %in% MIR_species ~ paste0("*", SPECIES_NAME),
    TRUE ~ as.character(SPECIES_NAME)))

#Factor re-order for aesthetics  
tmp_filtered <- tmp_filtered %>%
  mutate(SPECIES_NAME = 
           fct_reorder(SPECIES_NAME, if_else(is.na(PVbar), 0, PVbar), .desc = FALSE)) %>%
    mutate(analysis_group = recode(analysis_group,
                                 "MIR_GRP" = "MIR",
                                 "NCRMP_GRP" = "NCRMP + DRM"))

plt_bleach <- tmp_filtered %>%
  filter(SPECIES_NAME != "NA") %>%
  ggplot(aes(x = PVbar, y = SPECIES_NAME, fill = analysis_group)) +
  geom_col(position = position_dodge(width = 0.8), width = 0.8) +
  geom_errorbar(aes(xmin = PV_LCI, xmax = PV_UCI),
                position = position_dodge(width = 0.8), width = 0.1) +
  facet_wrap(~analysis_group) +
    labs(x = "Proportion of Colonies Bleached", y = "Coral Species", fill = "") +
    scale_x_continuous(expand = expansion(mult = c(0, 0.1))) +
        theme_Publication(base_size = 10) +
    coord_cartesian(clip = "off") +
        scale_y_discrete(labels = function(x) parse(text = paste0("italic('", x, "')"))) +
   scale_fill_manual(values = c("#0072B2", "#D55E00"), labels = c("MIR", "NCRMP + DRM")) +
    theme(
          strip.text = element_text(size = 14, face = "bold"),
          axis.text.y = element_text(size = 11),
          axis.title = element_text(size = 13),
          legend.position = "bottom",
          legend.text = element_text(size = 10),
          panel.spacing = unit(1, "lines")
        ) #+
  #ggtitle("2024 Estimates")


print(plt_bleach)

Figure 5. Estimated Observed Bleaching Frequency in 2024 using Ratio Estimaters

8 Disease Estimates

8.1 2022

Code
# Set path to data files
data_2022_path <- "../Project/data/FK2022_NCRMP_DRM_MIR_corsz_ARallv2.csv"


MIR_species <- c("ACR PALM", "ACR CERV", "COL NATA", "DEN CYLI", "DIC STOK", "DIP LABY", "EUS FAST", "MEA MEAN", "PSE CLIV", "PSE STRI", "MON CAVE")

# tmp <- disease_bleach_prevalence_ratio_est(data_2022_path, 2022) %>%
#   filter(PV_TYPE == "Bleach_All") %>%
#   left_join(., names)

names_clean <- names %>%
  select(SPECIES_CD, SPECIES_NAME) %>%
  distinct(SPECIES_CD, .keep_all = TRUE)

tmp_dis_2022 <- read_csv("../Project/data/fk2022_NCRMP_MIR_sppPVbar_Dall_dom.csv") %>%
    left_join(names_clean, by = "SPECIES_CD")

top_species <- tmp_dis_2022 %>%
  group_by(SPECIES_CD) %>%
  summarise(mean_pv = mean(PVbar, na.rm = TRUE)) %>%
  arrange(desc(mean_pv)) %>%
  slice_head(n = 10) %>%  
  pull(SPECIES_CD)

tmp_filtered <- tmp_dis_2022 %>%
  filter(SPECIES_CD %in% union(top_species, MIR_species)) 
  
##Add a * for those MIR species
tmp_filtered <- tmp_filtered %>% mutate(
  SPECIES_NAME = case_when(
    SPECIES_CD %in% MIR_species ~ paste0( "*", SPECIES_NAME),
    TRUE ~ as.character(SPECIES_NAME)))

#Factor re-order for aesthetics  
tmp_filtered <- tmp_filtered %>%
  mutate(SPECIES_NAME = 
           fct_reorder(SPECIES_NAME, if_else(is.na(PVbar), 0, PVbar), .desc = FALSE)) %>%
    mutate(analysis_group = recode(analysis_group,
                                 "MIR_GRP" = "MIR",
                                 "NCRMP_GRP" = "NCRMP + DRM"))

plt_disease <- tmp_filtered %>%
  filter(SPECIES_NAME != "NA") %>%
  ggplot(aes(x = PVbar, y = SPECIES_NAME, fill = analysis_group)) +
  geom_col(position = position_dodge(width = 0.8), width = 0.8) +
  geom_errorbar(
    aes(xmin = pmax(0, PV_LCI), xmax = PV_UCI),
    width = 0.15
  ) +
  facet_wrap(~analysis_group) +
    labs(x = "Proportion of Colonies Bleached", y = "Coral Species", fill = "") +
    scale_x_continuous(expand = expansion(mult = c(0, 0.1))) +
        theme_Publication(base_size = 10) +
    coord_cartesian(clip = "off") +
        scale_y_discrete(labels = function(x) parse(text = paste0("italic('", x, "')"))) +
   scale_fill_manual(values = c("#0072B2", "#D55E00"), labels = c("MIR", "NCRMP + DRM")) +
    theme(
          strip.text = element_text(size = 14, face = "bold"),
          axis.text.y = element_text(size = 11),
          axis.title = element_text(size = 13),
          legend.position = "bottom",
          legend.text = element_text(size = 10),
          panel.spacing = unit(1, "lines")
        ) #+
  #ggtitle("2022 Estimates")


print(plt_disease)

Figure 6 . Estimated Observed Disease Frequency in 2022 using Ratio Estimaters.

8.2 2024

Code
# Set path to data files
data_2024_path <- "../Project/data/FK2022_NCRMP_DRM_MIR_corsz_ARallv2.csv"


MIR_species <- c("ACR PALM", "ACR CERV", "COL NATA", "DEN CYLI", "DIC STOK", "DIP LABY", "EUS FAST", "MEA MEAN", "PSE CLIV", "PSE STRI", "MON CAVE")

# tmp <- disease_bleach_prevalence_ratio_est(data_2022_path, 2022) %>%
#   filter(PV_TYPE == "Bleach_All") %>%
#   left_join(., names)

names_clean <- names %>%
  select(SPECIES_CD, SPECIES_NAME) %>%
  distinct(SPECIES_CD, .keep_all = TRUE)

tmp_dis_2024 <- read_csv("../Project/data/fk2024_NCRMP_MIR_sppPVbar_Dall_dom.csv") %>%
    left_join(names_clean, by = "SPECIES_CD")


top_species <- tmp_dis_2024 %>%
  group_by(SPECIES_CD) %>%
  summarise(mean_pv = mean(PVbar, na.rm = TRUE)) %>%
  arrange(desc(mean_pv)) %>%
  slice_head(n = 10) %>%  
  pull(SPECIES_CD)

tmp_filtered <- tmp_dis_2024 %>%
  filter(SPECIES_CD %in% union(top_species, MIR_species)) 

##Add a * for those MIR species
tmp_filtered <- tmp_filtered %>% mutate(
  SPECIES_NAME = case_when(
    SPECIES_CD %in% MIR_species ~ paste0( "*", SPECIES_NAME),
    TRUE ~ as.character(SPECIES_NAME)))

#Factor re-order for aesthetics  
tmp_filtered <- tmp_filtered %>%
  mutate(SPECIES_NAME = 
           fct_reorder(SPECIES_NAME, if_else(is.na(PVbar), 0, PVbar), .desc = FALSE)) %>%
    mutate(analysis_group = recode(analysis_group,
                                 "MIR_GRP" = "MIR",
                                 "NCRMP_GRP" = "NCRMP + DRM"))

plt_dis <- tmp_filtered %>%
  filter(SPECIES_NAME != "NA") %>%
  ggplot(aes(x = PVbar, y = SPECIES_NAME, fill = analysis_group)) +
  geom_col(position = position_dodge(width = 0.8), width = 0.8) +
  geom_errorbar(
    aes(xmin = pmax(0, PV_LCI), xmax = PV_UCI),
    width = 0.15) +
  facet_wrap(~analysis_group) +
    labs(x = "Proportion of Colonies Bleached", y = "Coral Species", fill = "") +
    scale_x_continuous(expand = expansion(mult = c(0, 0.1))) +
        theme_Publication(base_size = 10) +
    coord_cartesian(clip = "off") +
        scale_y_discrete(labels = function(x) parse(text = paste0("italic('", x, "')"))) +
   scale_fill_manual(values = c("#0072B2", "#D55E00"), labels = c("MIR", "NCRMP + DRM")) +
    theme(
          strip.text = element_text(size = 14, face = "bold"),
          axis.text.y = element_text(size = 11),
          axis.title = element_text(size = 13),
          legend.position = "bottom",
          legend.text = element_text(size = 10),
          panel.spacing = unit(1, "lines")
        ) #+
 # ggtitle("2024 Disease Estimates")


print(plt_dis)

Figure 7 . Estimated Observed Disease Frequency in 2024 using Ratio Estimaters.

9 Overall Density Comparision

Figure 8. Caption needed here!

10 Overall Old Mortality Comparision

Figure 9. Caption needed here!

11 Overall Max Diameter Comparision

Figure 10. Caption needed here too!

12 References

Ault, J. S., Smith, S. G., Luo, J., Grove, L. J., Johnson, M. W., and Blondeau, J. (2021). Refinement of the southern Florida Reef Tract benthic habitat map with habitat use patterns of reef fish species. (NCEI Accession 0224176). NOAA National Centers for Environmental Information. Dataset. https://www.ncei.noaa.gov/archive/accession/0224176 NOAA Coral Reef Conservation Program. (2022b).

National Coral Reef Monitoring Program (NCRMP) benthic community assessment survey field protocols for U.S. Atlantic: Florida, Flower Garden Banks, Puerto Rico, and U.S. Virgin Islands–2022. NOAA Coral Reef Conservation Program. https://doi.org/0.25923/0708-8333

NOAA Coral Reef Conservation Program. (2022c). National Coral Reef Monitoring Program (NCRMP) coral demographics survey field protocols for U.S. Atlantic: Florida, Flower Garden Banks, Puerto Rico, U.S. Virgin Islands. 2022. NOAA Coral Reef Conservation Program. https://doi.org/10.25923/9a1r-m911

NOAA National Marine Fisheries Service. (2014). Final rule. Endangered and threatened wildlife and plants: Final listing determinations on proposal to list 66 reef-building coral species and to reclassify elkhorn and staghorn corals. Federal Register, 79(175), 53851–54123.

insert fish tech memo referenced earlier

also missing some of the field protocols?